基于文化粒子群算法的水库防洪优化调度

Particle swarm optimization based on cultural algorithm for flood optimal scheduling of hydropower reservoir

针对水库防洪调度具有多约束、高维、非线性和不易求解的特点,在粒子群算法(PSO)的进化机制中引入文化算法(CA),利用文化算法的信仰空间对粒子群的进化进行指导,建立了文化粒子群算法(PSO-CA)。该算法在种群空间中采用PSO算法,在信仰空间中利用从种群空间中提炼出来的群体经验所形成的形势知识对群体的"早熟"现象进行监视,利用规范化知识对粒子加以限定,提高了算法计算效率。实例验证,该算法能较好地克服粒子群算法易陷入局部最优的缺点,并加快其收敛速度,可以有效解决水库防洪调度问题。

In order to solve multi-restriction,multi-dimension,non-linear and other difficult problems of flood optimal scheduling,a novel swarm optimization algorithm,namely Particle Swarm Optimization,based on Cultural Algorithm,is presented in this paper.This algorithm guides the evolutionary mechanism of PSO in population space.It keeps a watchful eye on the prematurity by shape knowledge which obtained by the group experience and limits the swarm by standardization knowledge in belief space.The effectiveness such as avoiding the local optimization and increasing the speed of convergence of this method is verified by practical application.